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KLASIFIKASI TUBERKULOSIS PADA CITRA CHEST X-RAY MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

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Penilaian

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Tuberculosis is still one of the infectious diseases that causes high mortality rates worldwide. Early detection using Chest X-Ray images is very important in the effort to treat this disease. However, manual interpretation of X-ray results performed by doctors can cause differences in diagnosis. This study aims to classify tuberculosis in Chest X-Ray images by implementing three Convolutional Neural Network (CNN) architectures, namely VGG-19, ResNet-50, and DenseNet-121. This study uses a dataset of 7,000 images consisting of 3,500 normal images and 3,500 tuberculosis images. Experiments were conducted using 12 test scenarios with a combination of hyperparameter learning rate (0.01 and 0.001) and batch size (32 and 64) on each architecture. The results showed that the best model was achieved by VGG-19 architecture with a combination of learning rate 0.001 and batch size 32, which produced the highest performance with accuracy values of 99.57%, precision 99.71%, recall 99.43%, and F1-score 99.57%. This research proves that proper CNN implementation with optimal configuration of hyperparameters can be an effective tool in detecting tuberculosis through Chest X-Ray images.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003330	T175011	T1750112025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1750112025

Publisher

: Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiv, 64 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

EM

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